|Tracking moving objects in video sequences is a task that emerges in various fields of study: video analysis, computer vision, biomedical systems etc. In the last decade, special attention has been drawn to problems concerning tracking in real-world environments, where moving objects do not obey any afore-known constraints about their nature and motion or the scenes they are moving in. Apart from the existence of noise and environmental changes, many problems are also concerned, due to background texture, complicated object motion, and deformable and/or articulated objects, changing their shape while moving along time. Another phenomenon in natural sequences is the appearance of occlusions between different objects, whose handling requires motion information and, in some cases, additional constraints. In this work, we revisit one of the most known active contours, the Snakes, and we propose a motion-based utilization of it, aiming at successful handling of the previously mentioned problems. The use of the object motion history and first order statistical measurements of it, provide us with information for the extraction of uncertainty regions, a kind of shape prior knowledge w.r.t. the allowed object deformations. This constraining also makes the proposed method ecient, handling the trade-off between accuracy and computation complexity. The energy minimization is approximated by a force-based approach inside the extracted uncertainty regions, and the weights of the total snake energy function are automatically estimated as respective weights in the resulting evolution force. Finally, in order to handle background complexity and partial occlusion cases, we introduce two rules, according to which the moving object region is correctly separated from the background, whereas the occluded boundaries are estimated according to the object's expected shape. To verify the performance of the proposed method, some experimental results are included, concerning dierent cases of object tracking, indoors and outdoors, with rigid and deformable objects, noisy and textured backgrounds, as well as appearance of occlusions.
|G. Tsechpenakis, K. Rapantzikos, N. Tsapatsoulis and S. Kollias, "A Snake Model for Object Tracking in Natural Sequences", Elsevier, Signal Processing: Image Communication, Volume 19, Issue 3, March 2004, Pages 219-238 |